Feature engineering is a critical part of end-to-end learning pipelines in many practical supervised learning settings. While the most predictive features often build off of diverse domain expertise and human intuition, rarely are more than a small handful of data scientists and researchers involved in this process. Ballet addresses this problem by providing a framework for scaling feature engineering collaborations in an open-source setting. In our approach, collaborators incrementally submit patches containing standalone feature definitions to a central source code repository. Our framework provides functionality for composing the separate features into an executable end-to-end pipeline, evaluating feature submissions in a streaming fashion, and automating project management tasks for maintainers. In this demonstration, audience participants will collaborate in real-time in a feature engineering task on a complex, real-world dataset.